Feature Effect Evaluation¶

PDP¶

Error of Model-PD compared to groundtruth-PD¶

In [4]:
effects_results_storage = config.get("storage", "effects_results")
df = pd.read_sql_table("pdp_results", f"sqlite:///..{effects_results_storage}")
df
Out[4]:
index model_id model simulation n_train noise_sd metric x_1 x_2 x_3 x_4 x_5
0 0 RandomForestRegressor_20240413_1_1000_0.1 RandomForestRegressor 1 1000 0.1 mean_squared_error 0.178899 0.203574 0.672312 0.110749 0.088638
1 0 XGBRegressor_20240413_1_1000_0.1 XGBRegressor 1 1000 0.1 mean_squared_error 0.016875 0.015341 0.020013 0.017393 0.006096
2 0 DecisionTreeRegressor_20240413_1_1000_0.1 DecisionTreeRegressor 1 1000 0.1 mean_squared_error 0.187063 0.168292 0.797859 0.159578 0.091377
3 0 SVR_20240413_1_1000_0.1 SVR 1 1000 0.1 mean_squared_error 0.000020 0.000053 0.000271 0.000044 0.000185
4 0 ElasticNet_20240413_1_1000_0.1 ElasticNet 1 1000 0.1 mean_squared_error 0.851548 0.953455 2.296132 0.000909 0.001021
5 0 GAM_20240413_1_1000_0.1 GAM 1 1000 0.1 mean_squared_error 0.000283 0.000279 0.000105 0.000311 0.000201
6 0 RandomForestRegressor_20240413_1_1000_0.5 RandomForestRegressor 1 1000 0.5 mean_squared_error 0.186385 0.211262 0.643921 0.101243 0.095049
7 0 XGBRegressor_20240413_1_1000_0.5 XGBRegressor 1 1000 0.5 mean_squared_error 0.021464 0.024066 0.027774 0.021291 0.013897
8 0 DecisionTreeRegressor_20240413_1_1000_0.5 DecisionTreeRegressor 1 1000 0.5 mean_squared_error 0.137864 0.300960 0.656510 0.134170 0.128621
9 0 SVR_20240413_1_1000_0.5 SVR 1 1000 0.5 mean_squared_error 0.000672 0.000998 0.001899 0.000696 0.001844
10 0 ElasticNet_20240413_1_1000_0.5 ElasticNet 1 1000 0.5 mean_squared_error 0.851724 0.953386 2.298243 0.001387 0.001388
11 0 GAM_20240413_1_1000_0.5 GAM 1 1000 0.5 mean_squared_error 0.001042 0.001591 0.002211 0.000689 0.000812
12 0 RandomForestRegressor_20240413_2_1000_0.1 RandomForestRegressor 2 1000 0.1 mean_squared_error 0.148672 0.209911 0.646148 0.246621 0.108333
13 0 XGBRegressor_20240413_2_1000_0.1 XGBRegressor 2 1000 0.1 mean_squared_error 0.015571 0.020568 0.023027 0.025449 0.011853
14 0 DecisionTreeRegressor_20240413_2_1000_0.1 DecisionTreeRegressor 2 1000 0.1 mean_squared_error 0.099713 0.244102 0.755546 0.243398 0.139652
15 0 SVR_20240413_2_1000_0.1 SVR 2 1000 0.1 mean_squared_error 0.000022 0.000057 0.000127 0.000123 0.000039
16 0 ElasticNet_20240413_2_1000_0.1 ElasticNet 2 1000 0.1 mean_squared_error 1.033813 0.909835 2.302066 0.000719 0.000694
17 0 GAM_20240413_2_1000_0.1 GAM 2 1000 0.1 mean_squared_error 0.000525 0.000353 0.000170 0.000855 0.000140
18 0 RandomForestRegressor_20240413_2_1000_0.5 RandomForestRegressor 2 1000 0.5 mean_squared_error 0.151655 0.214597 0.626806 0.253600 0.113697
19 0 XGBRegressor_20240413_2_1000_0.5 XGBRegressor 2 1000 0.5 mean_squared_error 0.015402 0.024618 0.032997 0.031710 0.017810
20 0 DecisionTreeRegressor_20240413_2_1000_0.5 DecisionTreeRegressor 2 1000 0.5 mean_squared_error 0.155236 0.189773 0.943644 0.249308 0.165510
21 0 SVR_20240413_2_1000_0.5 SVR 2 1000 0.5 mean_squared_error 0.002258 0.002048 0.001367 0.002167 0.001201
22 0 ElasticNet_20240413_2_1000_0.5 ElasticNet 2 1000 0.5 mean_squared_error 1.032929 0.909313 2.303330 0.003641 0.000981
23 0 GAM_20240413_2_1000_0.5 GAM 2 1000 0.5 mean_squared_error 0.001076 0.001281 0.002035 0.003880 0.000032
24 0 RandomForestRegressor_20240413_3_1000_0.1 RandomForestRegressor 3 1000 0.1 mean_squared_error 0.138445 0.190675 0.881256 0.168130 0.129340
25 0 XGBRegressor_20240413_3_1000_0.1 XGBRegressor 3 1000 0.1 mean_squared_error 0.013733 0.011819 0.021524 0.015494 0.009011
26 0 DecisionTreeRegressor_20240413_3_1000_0.1 DecisionTreeRegressor 3 1000 0.1 mean_squared_error 0.208063 0.392589 0.704313 0.260510 0.124586
27 0 SVR_20240413_3_1000_0.1 SVR 3 1000 0.1 mean_squared_error 0.000197 0.000151 0.000109 0.000019 0.000020
28 0 ElasticNet_20240413_3_1000_0.1 ElasticNet 3 1000 0.1 mean_squared_error 0.969283 0.908738 2.296879 0.002681 0.007561
29 0 GAM_20240413_3_1000_0.1 GAM 3 1000 0.1 mean_squared_error 0.000303 0.000099 0.000234 0.000564 0.000141
30 0 RandomForestRegressor_20240413_3_1000_0.5 RandomForestRegressor 3 1000 0.5 mean_squared_error 0.137355 0.190433 0.890642 0.166069 0.132726
31 0 XGBRegressor_20240414_3_1000_0.5 XGBRegressor 3 1000 0.5 mean_squared_error 0.019530 0.031508 0.025388 0.021775 0.013518
32 0 DecisionTreeRegressor_20240414_3_1000_0.5 DecisionTreeRegressor 3 1000 0.5 mean_squared_error 0.162907 0.347728 0.773309 0.223763 0.130480
33 0 SVR_20240414_3_1000_0.5 SVR 3 1000 0.5 mean_squared_error 0.000558 0.002804 0.001020 0.000614 0.000832
34 0 ElasticNet_20240414_3_1000_0.5 ElasticNet 3 1000 0.5 mean_squared_error 0.968399 0.908582 2.296770 0.002834 0.007601
35 0 GAM_20240414_3_1000_0.5 GAM 3 1000 0.5 mean_squared_error 0.001016 0.000920 0.004985 0.000800 0.000165
36 0 RandomForestRegressor_20240414_4_1000_0.1 RandomForestRegressor 4 1000 0.1 mean_squared_error 0.179592 0.193935 0.946746 0.133848 0.152169
37 0 XGBRegressor_20240414_4_1000_0.1 XGBRegressor 4 1000 0.1 mean_squared_error 0.015688 0.024212 0.030230 0.015045 0.011537
38 0 DecisionTreeRegressor_20240414_4_1000_0.1 DecisionTreeRegressor 4 1000 0.1 mean_squared_error 0.192033 0.162543 1.002283 0.193901 0.105449
39 0 SVR_20240414_4_1000_0.1 SVR 4 1000 0.1 mean_squared_error 0.000056 0.000135 0.000334 0.000082 0.000161
40 0 ElasticNet_20240414_4_1000_0.1 ElasticNet 4 1000 0.1 mean_squared_error 1.051079 1.036128 2.296727 0.001188 0.000910
41 0 GAM_20240414_4_1000_0.1 GAM 4 1000 0.1 mean_squared_error 0.000215 0.000419 0.000269 0.000514 0.000174
42 0 RandomForestRegressor_20240414_4_1000_0.5 RandomForestRegressor 4 1000 0.5 mean_squared_error 0.172444 0.198991 0.975957 0.109655 0.161776
43 0 XGBRegressor_20240414_4_1000_0.5 XGBRegressor 4 1000 0.5 mean_squared_error 0.024328 0.031237 0.032734 0.018225 0.017903
44 0 DecisionTreeRegressor_20240414_4_1000_0.5 DecisionTreeRegressor 4 1000 0.5 mean_squared_error 0.180456 0.170292 0.867263 0.193355 0.117926
45 0 SVR_20240414_4_1000_0.5 SVR 4 1000 0.5 mean_squared_error 0.000793 0.002795 0.001837 0.002153 0.002939
46 0 ElasticNet_20240414_4_1000_0.5 ElasticNet 4 1000 0.5 mean_squared_error 1.056593 1.050310 2.297129 0.001332 0.002119
47 0 GAM_20240414_4_1000_0.5 GAM 4 1000 0.5 mean_squared_error 0.000784 0.002298 0.004507 0.000387 0.000618
48 0 RandomForestRegressor_20240414_5_1000_0.1 RandomForestRegressor 5 1000 0.1 mean_squared_error 0.265733 0.138409 0.731703 0.204957 0.149158
49 0 XGBRegressor_20240414_5_1000_0.1 XGBRegressor 5 1000 0.1 mean_squared_error 0.017781 0.012037 0.028900 0.021594 0.012021
50 0 DecisionTreeRegressor_20240414_5_1000_0.1 DecisionTreeRegressor 5 1000 0.1 mean_squared_error 0.137495 0.268633 0.916016 0.227593 0.142377
51 0 SVR_20240414_5_1000_0.1 SVR 5 1000 0.1 mean_squared_error 0.000176 0.000137 0.000285 0.000107 0.000255
52 0 ElasticNet_20240414_5_1000_0.1 ElasticNet 5 1000 0.1 mean_squared_error 0.964777 0.999703 2.300356 0.000762 0.007345
53 0 GAM_20240414_5_1000_0.1 GAM 5 1000 0.1 mean_squared_error 0.000326 0.000252 0.000587 0.000428 0.000141
54 0 RandomForestRegressor_20240414_5_1000_0.5 RandomForestRegressor 5 1000 0.5 mean_squared_error 0.279758 0.131032 0.662782 0.195756 0.151572
55 0 XGBRegressor_20240414_5_1000_0.5 XGBRegressor 5 1000 0.5 mean_squared_error 0.021744 0.014687 0.030154 0.026170 0.013582
56 0 DecisionTreeRegressor_20240414_5_1000_0.5 DecisionTreeRegressor 5 1000 0.5 mean_squared_error 0.151106 0.169537 0.756001 0.247949 0.111059
57 0 SVR_20240414_5_1000_0.5 SVR 5 1000 0.5 mean_squared_error 0.000602 0.001949 0.004220 0.001232 0.000363
58 0 ElasticNet_20240414_5_1000_0.5 ElasticNet 5 1000 0.5 mean_squared_error 0.964049 0.999738 2.302917 0.000776 0.006847
59 0 GAM_20240414_5_1000_0.5 GAM 5 1000 0.5 mean_squared_error 0.001349 0.001290 0.010604 0.001113 0.000435
In [5]:
%matplotlib inline
boxplot_feature_effect_results(features=["x_1", "x_2", "x_3", "x_4", "x_5"], df=df, effect_type="PDP");
No description has been provided for this image

PDP example visualizations¶

In [6]:
md(f"(simulation no. {sim_no} with n_train={n_train} and noise_sd={noise_sd})")
Out[6]:

(simulation no. 1 with n_train=1000 and noise_sd=0.1)

In [8]:
%matplotlib inline
plot_effect_comparison(rf, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [9]:
%matplotlib inline
plot_effect_comparison(xgb, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [10]:
%matplotlib inline
plot_effect_comparison(tree, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [11]:
%matplotlib inline
plot_effect_comparison(svm, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [12]:
%matplotlib inline
plot_effect_comparison(elasticnet, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [13]:
%matplotlib inline
plot_effect_comparison(gam, groundtruth, X_train, effect="PDP", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image

ALE¶

Error of Model-ALE compared to groundtruth-ALE¶

In [14]:
effects_results_storage = config.get("storage", "effects_results")
df = pd.read_sql_table("ale_results", f"sqlite:///..{effects_results_storage}")
df
Out[14]:
index model_id model simulation n_train noise_sd metric x_1 x_2 x_3 x_4 x_5
0 0 RandomForestRegressor_20240413_1_1000_0.1 RandomForestRegressor 1 1000 0.1 mean_squared_error 0.102209 0.078024 0.504765 0.045459 0.022509
1 0 XGBRegressor_20240413_1_1000_0.1 XGBRegressor 1 1000 0.1 mean_squared_error 0.088313 0.032658 0.044790 0.033427 0.033133
2 0 DecisionTreeRegressor_20240413_1_1000_0.1 DecisionTreeRegressor 1 1000 0.1 mean_squared_error 0.450111 0.341412 0.613983 2.657984 0.412017
3 0 SVR_20240413_1_1000_0.1 SVR 1 1000 0.1 mean_squared_error 0.000037 0.000040 0.000218 0.000056 0.000214
4 0 ElasticNet_20240413_1_1000_0.1 ElasticNet 1 1000 0.1 mean_squared_error 0.856908 1.085617 2.223995 0.000952 0.001000
5 0 GAM_20240413_1_1000_0.1 GAM 1 1000 0.1 mean_squared_error 0.000334 0.000265 0.000089 0.000320 0.000190
6 0 RandomForestRegressor_20240413_1_1000_0.5 RandomForestRegressor 1 1000 0.5 mean_squared_error 0.107340 0.078632 0.481248 0.041882 0.039674
7 0 XGBRegressor_20240413_1_1000_0.5 XGBRegressor 1 1000 0.5 mean_squared_error 0.094882 0.087883 0.099508 0.028062 0.082043
8 0 DecisionTreeRegressor_20240413_1_1000_0.5 DecisionTreeRegressor 1 1000 0.5 mean_squared_error 0.332879 0.326019 0.512040 0.473676 0.305222
9 0 SVR_20240413_1_1000_0.5 SVR 1 1000 0.5 mean_squared_error 0.000317 0.001205 0.001456 0.000398 0.001531
10 0 ElasticNet_20240413_1_1000_0.5 ElasticNet 1 1000 0.5 mean_squared_error 0.856576 1.088063 2.224006 0.000951 0.000866
11 0 GAM_20240413_1_1000_0.5 GAM 1 1000 0.5 mean_squared_error 0.000803 0.001239 0.001782 0.000254 0.000386
12 0 RandomForestRegressor_20240413_2_1000_0.1 RandomForestRegressor 2 1000 0.1 mean_squared_error 0.108499 0.088505 0.553368 0.113833 0.017661
13 0 XGBRegressor_20240413_2_1000_0.1 XGBRegressor 2 1000 0.1 mean_squared_error 0.069223 0.066510 0.016483 0.031998 0.052082
14 0 DecisionTreeRegressor_20240413_2_1000_0.1 DecisionTreeRegressor 2 1000 0.1 mean_squared_error 0.758521 0.379145 0.758179 1.960838 0.104751
15 0 SVR_20240413_2_1000_0.1 SVR 2 1000 0.1 mean_squared_error 0.000037 0.000046 0.000136 0.000176 0.000047
16 0 ElasticNet_20240413_2_1000_0.1 ElasticNet 2 1000 0.1 mean_squared_error 0.944539 0.992477 2.259133 0.000714 0.000681
17 0 GAM_20240413_2_1000_0.1 GAM 2 1000 0.1 mean_squared_error 0.000256 0.000292 0.000179 0.000849 0.000138
18 0 RandomForestRegressor_20240413_2_1000_0.5 RandomForestRegressor 2 1000 0.5 mean_squared_error 0.087106 0.091529 0.559728 0.105091 0.022528
19 0 XGBRegressor_20240413_2_1000_0.5 XGBRegressor 2 1000 0.5 mean_squared_error 0.052993 0.062248 0.022821 0.032069 0.044360
20 0 DecisionTreeRegressor_20240413_2_1000_0.5 DecisionTreeRegressor 2 1000 0.5 mean_squared_error 0.657025 2.455691 0.773211 0.891441 0.239885
21 0 SVR_20240413_2_1000_0.5 SVR 2 1000 0.5 mean_squared_error 0.001267 0.001508 0.000990 0.001604 0.001187
22 0 ElasticNet_20240413_2_1000_0.5 ElasticNet 2 1000 0.5 mean_squared_error 0.943744 0.991152 2.261525 0.003615 0.000959
23 0 GAM_20240413_2_1000_0.5 GAM 2 1000 0.5 mean_squared_error 0.000709 0.001130 0.002092 0.003852 0.000032
24 0 RandomForestRegressor_20240413_3_1000_0.1 RandomForestRegressor 3 1000 0.1 mean_squared_error 0.084576 0.174632 0.687282 0.080984 0.044223
25 0 XGBRegressor_20240413_3_1000_0.1 XGBRegressor 3 1000 0.1 mean_squared_error 0.051716 0.009275 0.013223 0.032149 0.023333
26 0 DecisionTreeRegressor_20240413_3_1000_0.1 DecisionTreeRegressor 3 1000 0.1 mean_squared_error 0.456843 0.483704 0.601405 0.296938 0.217799
27 0 SVR_20240413_3_1000_0.1 SVR 3 1000 0.1 mean_squared_error 0.000176 0.000246 0.000082 0.000033 0.000023
28 0 ElasticNet_20240413_3_1000_0.1 ElasticNet 3 1000 0.1 mean_squared_error 1.028461 0.980727 2.227729 0.002826 0.007909
29 0 GAM_20240413_3_1000_0.1 GAM 3 1000 0.1 mean_squared_error 0.000408 0.000215 0.000224 0.000595 0.000148
30 0 RandomForestRegressor_20240413_3_1000_0.5 RandomForestRegressor 3 1000 0.5 mean_squared_error 0.097168 0.163768 0.701964 0.081795 0.051585
31 0 XGBRegressor_20240414_3_1000_0.5 XGBRegressor 3 1000 0.5 mean_squared_error 0.027236 0.026640 0.015414 0.031269 0.022480
32 0 DecisionTreeRegressor_20240414_3_1000_0.5 DecisionTreeRegressor 3 1000 0.5 mean_squared_error 0.301233 0.257206 1.141532 0.448084 0.425581
33 0 SVR_20240414_3_1000_0.5 SVR 3 1000 0.5 mean_squared_error 0.000716 0.003037 0.001180 0.000910 0.000937
34 0 ElasticNet_20240414_3_1000_0.5 ElasticNet 3 1000 0.5 mean_squared_error 1.022676 0.980959 2.227729 0.002991 0.007931
35 0 GAM_20240414_3_1000_0.5 GAM 3 1000 0.5 mean_squared_error 0.000905 0.001394 0.004704 0.000845 0.000172
36 0 RandomForestRegressor_20240414_4_1000_0.1 RandomForestRegressor 4 1000 0.1 mean_squared_error 0.067455 0.131734 0.796294 0.037615 0.065405
37 0 XGBRegressor_20240414_4_1000_0.1 XGBRegressor 4 1000 0.1 mean_squared_error 0.058053 0.071941 0.015962 0.037490 0.089937
38 0 DecisionTreeRegressor_20240414_4_1000_0.1 DecisionTreeRegressor 4 1000 0.1 mean_squared_error 0.251189 0.250814 0.793227 0.414049 1.172958
39 0 SVR_20240414_4_1000_0.1 SVR 4 1000 0.1 mean_squared_error 0.000075 0.000123 0.000288 0.000091 0.000137
40 0 ElasticNet_20240414_4_1000_0.1 ElasticNet 4 1000 0.1 mean_squared_error 1.225967 0.968002 2.184606 0.001146 0.000868
41 0 GAM_20240414_4_1000_0.1 GAM 4 1000 0.1 mean_squared_error 0.000220 0.000426 0.000248 0.000495 0.000164
42 0 RandomForestRegressor_20240414_4_1000_0.5 RandomForestRegressor 4 1000 0.5 mean_squared_error 0.060786 0.146011 0.788864 0.036007 0.079909
43 0 XGBRegressor_20240414_4_1000_0.5 XGBRegressor 4 1000 0.5 mean_squared_error 0.060250 0.068185 0.041078 0.048566 0.063810
44 0 DecisionTreeRegressor_20240414_4_1000_0.5 DecisionTreeRegressor 4 1000 0.5 mean_squared_error 0.795661 0.524065 0.511875 0.671951 0.346964
45 0 SVR_20240414_4_1000_0.5 SVR 4 1000 0.5 mean_squared_error 0.000692 0.002709 0.001586 0.002086 0.002741
46 0 ElasticNet_20240414_4_1000_0.5 ElasticNet 4 1000 0.5 mean_squared_error 1.230699 0.978949 2.184610 0.001238 0.001969
47 0 GAM_20240414_4_1000_0.5 GAM 4 1000 0.5 mean_squared_error 0.000418 0.002201 0.004158 0.000320 0.000533
48 0 RandomForestRegressor_20240414_5_1000_0.1 RandomForestRegressor 5 1000 0.1 mean_squared_error 0.082736 0.110605 0.610456 0.152503 0.071705
49 0 XGBRegressor_20240414_5_1000_0.1 XGBRegressor 5 1000 0.1 mean_squared_error 0.137153 0.118048 0.031283 0.070292 0.065766
50 0 DecisionTreeRegressor_20240414_5_1000_0.1 DecisionTreeRegressor 5 1000 0.1 mean_squared_error 0.452512 0.360682 1.111724 1.219658 0.374757
51 0 SVR_20240414_5_1000_0.1 SVR 5 1000 0.1 mean_squared_error 0.000037 0.000121 0.000236 0.000031 0.000208
52 0 ElasticNet_20240414_5_1000_0.1 ElasticNet 5 1000 0.1 mean_squared_error 0.961997 1.045239 2.380695 0.000765 0.007340
53 0 GAM_20240414_5_1000_0.1 GAM 5 1000 0.1 mean_squared_error 0.000359 0.000281 0.000510 0.000422 0.000125
54 0 RandomForestRegressor_20240414_5_1000_0.5 RandomForestRegressor 5 1000 0.5 mean_squared_error 0.086741 0.107020 0.513056 0.141217 0.042970
55 0 XGBRegressor_20240414_5_1000_0.5 XGBRegressor 5 1000 0.5 mean_squared_error 0.055380 0.080611 0.047591 0.095212 0.016603
56 0 DecisionTreeRegressor_20240414_5_1000_0.5 DecisionTreeRegressor 5 1000 0.5 mean_squared_error 0.598115 0.313542 0.728063 4.416162 0.187126
57 0 SVR_20240414_5_1000_0.5 SVR 5 1000 0.5 mean_squared_error 0.000359 0.002813 0.004640 0.001000 0.000200
58 0 ElasticNet_20240414_5_1000_0.5 ElasticNet 5 1000 0.5 mean_squared_error 0.960468 1.047228 2.386402 0.000374 0.006440
59 0 GAM_20240414_5_1000_0.5 GAM 5 1000 0.5 mean_squared_error 0.001030 0.001030 0.009240 0.000720 0.000024
In [15]:
%matplotlib inline
boxplot_feature_effect_results(features=["x_1", "x_2", "x_3", "x_4", "x_5"], df=df, effect_type="ALE");
No description has been provided for this image

ALE example visualizations¶

In [16]:
md(f"(simulation no. {sim_no} with n_train={n_train} and noise_sd={noise_sd})")
Out[16]:

(simulation no. 1 with n_train=1000 and noise_sd=0.1)

In [17]:
%matplotlib inline
plot_effect_comparison(rf, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [18]:
%matplotlib inline
plot_effect_comparison(xgb, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [19]:
%matplotlib inline
plot_effect_comparison(tree, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [20]:
%matplotlib inline
plot_effect_comparison(svm, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [21]:
%matplotlib inline
plot_effect_comparison(elasticnet, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image
In [22]:
%matplotlib inline
plot_effect_comparison(gam, groundtruth, X_train, effect="ALE", features=['x_1', "x_2", "x_3", "x_4", "x_5"], config=config);
No description has been provided for this image